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| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import torch.nn.functional as F | |
| import os | |
| from huggingface_hub import login | |
| def run_model(input_text) : | |
| #classifier = pipeline('sentiment-analysis') | |
| #response = classifier('I have been waiting for a HuggingFace course my whole life!') | |
| #print(response) | |
| #outputs = response | |
| #generator = pipeline("text-generation", model = "distilgpt2") | |
| #response = generator("In this course, we will teach you how to", | |
| # max_length = 30, | |
| # num_return_sequences = 2 | |
| # ) | |
| #print (response) | |
| #classifier = pipeline("zero-shot-classification") | |
| #response = classifier( | |
| # "This is a course about Python list comprehension", | |
| # candidate_labels = ["education", "politics", "business" | |
| # ] | |
| # ) | |
| #print(response) | |
| #tokenizer = AutoTokenizer.from_pretrained("D:\gouri_docs\generative_AI\llama\llama") | |
| #model = AutoModelForSequenceClassification.from_pretrained("D:\gouri_docs\generative_AI\llama\llama") | |
| ##input_text = "Hello Llama! How are you?" | |
| #inputs = tokenizer.encode(input_text, return_tensors="pt") | |
| #outputs = model.generate( | |
| # inputs, | |
| # max_length=50, | |
| # num_return_sequences=5, | |
| # temperature=0.7 | |
| # ) | |
| #print("Generated Text:") | |
| #for i, output in enumerate(outputs): | |
| # print(f"{i}: {tokenizer.decode(output)}") | |
| model_name = "meta-llama/Llama-2-70b-chat-hf" #"distilbert-base-uncased-finetuned-sst-2-english" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| #process.env.HF_ACCESS_TOKEN = HF_TOKEN | |
| #os.environ["HF_ACCESS_TOKEN"] = HF_TOKEN | |
| login(token = HF_TOKEN) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name, token=HF_TOKEN) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| text_Completion = pipeline( | |
| 'text-generation', #'sentiment-analysis', | |
| model=model, | |
| tokenizer=tokenizer) | |
| x_train = ['I have been waiting for a HuggingFace course my whole life!', | |
| 'Python is great!'] | |
| response = text_Completion(x_train) | |
| print(response) | |
| outputs = response | |
| #batch = tokenizer(x_train, padding = True, truncation = True, max_length = 512, return_tensors = 'pt') | |
| #print('\n\nbatch =', batch, '\n\n') | |
| #with torch.no_grad(): | |
| # outputs = model(**batch) | |
| # print('outputs = ', outputs) | |
| # predictions = F.softmax(outputs.logits, dim=1) | |
| # print('predictions =', predictions) | |
| # labels = torch.argmax(predictions, dim=1) | |
| # print('labels =', labels, '\n\n') | |
| return outputs |